Patent Application
20190282125
The inventive subject matter disclosed herein relates to deriving respiration rates of a subject (e.g., a human patient) from optically-based physiological sensor devices, such as a pulse oximeter, that produce an output in the form of a photoplethysmogram (PPG).
A wide range of devices exist that depend upon the transmission of optical signals to monitor or measure various biological or environmental parameters of a patient. For example, various forms of blood oximetry devices employ the transmission and reception of signals in the measurement of one or more biological or environmental parameters of a patient.
Blood oximetry devices, or pulse oximeters, are commonly used to monitor or measure oxygen saturation levels of blood in a body organ or tissues, including blood vessels, or the oxidative metabolism of tissues or organs. An example of an optical oximeter is disclosed in U.S. Pat. No. Re 33,643, entitled “Single Channel Pulse Oximeter.” Pulse oximetry is a technology used to measure the oxygen level in a subject's blood as well as the subject's heart rate. A finger pulse oximeter is equipped with technology to rapidly detect changes in the subject's blood oxygen level. These devices are also often capable of and are used to determine pulse rate and volume of blood flow in organs or tissues, or to monitor or measure other biological or environmental parameters.
A blood oximetry device measures the levels of the components of one or more signals of one or more frequencies as transmitted through or reflected from tissue or an organ to determine one or more biological or environmental parameters, such as blood oxygenation level and blood volume or pulse rate of a patient.
Additionally, respiration affects cardiac cycles by varying the intrathoracic pressure within the pleural cavity of an animal (e.g., a human) subject. The intrathoracic pressure is the pressure between the thoracic wall and the lungs. Since the heart resides in the thoracic cavity between the lungs, the partial pressure due to inhalation and exhalation during breathing influences the pressure on the venae cavae. Therefore, since respiration affects the cardiac cycle, the PPG contains signal components caused by the respiratory cycles of inhaled and exhaled breaths. Consequently, the PPG signal contains information that may be extracted to determine the subject's respiration rate in breaths per minute (BPM).
Blood oximetry devices may also be constructed as directly connected devices, that is, devices that are connected directly to a patient and that directly present the desired information or directly record the information, and as remote devices, that is, devices attached to a patient and transmitting the measurements to a remote display, monitoring or data collection device.
Blood oximetry devices measure blood oxygen levels, pulse rate, and volume of blood flow by emitting radiation in a frequency range, such as the red or near infrared range, wherein the transmission of the radiation through or reflectance of the radiation from the tissues or organ is measurably affected by the oxygen saturation levels and volume of the blood in the tissues or organ. A measurement of the signal level transmitted through a tissue or organ or reflected from a tissue or organ may then provide a measurement or indication of the oxygen saturation level in the tissue or organ. The transmitted or reflected signals may be of different frequencies which are typically affected in measurably different ways or amounts by various parameters or factors or components of the blood.
Parameters represented by transmitted or reflected signals may be represented by different and related or unrelated parameters of the received signals. For example, a signal transmitted through or reflected from tissue or an organ to measure, for example, blood oxygenation or flow, may have a constant or “DC” component due to the steady state volume of blood in the tissue or organ and time varying or “AC” components indicative of the time varying volume of blood flowing through the tissue or organ due to the heart beat of the body. Each signal component may provide different information, and may provide information that may be used together to generate or determine further information. What is needed is a way to determine quickly and accurately the respiration rate of a subject (e.g., a human patient) using date from the PPG.
As discussed above, changes in intrathoracic pressure during respiration cycles cause modulations to a PPG signal. In
Referring now to
For example,
All three of these fundamental signals, DC, pM, and pT, are used substantially concurrently to extract an actual respiration rate of a subject (e.g., a patient). The signal-to-noise ratio (SNR) of these three fundamental signals can vary greatly from one subject to another. For example, some subjects may have a high SNR for all three signals. For other subjects, only one of the three signals may have an SNR that is sufficiently high to extract a respiratory rate. For a small percentage of the population, none of the three signals has a high SNR. Therefore, by considering each of the three fundamental signals, a true respiration rate can be extracted for all or nearly all subjects.
Various embodiments of the inventive subject matter presented herein consider zero-crossings of the signals in a time domain. As discussed in detail below, the SNR of each of the three fundamental signals is increased or maximized by using an adaptive filter that is tuned to the time-dependent signal. Consequently, a determination is made as to the approximate frequency of the signal. The signal is then passed through a filter that is closely matched, in time, to the signal. By matching the filter width, in time, to the signal, the SNR is increased or maximized. For example, if the sampling window of the filter is too wide, extra noise is introduced. If the width is too small, the signal cannot be resolved in time.
A key parameter used in extraction of the respiration rate is beta (β). β is defined as the breath frequency when sampling at the pulse rate and is given by the following equation:
Therefore, as shown by equation (1), β is the frequency of the respiration rate in pulse time (as opposed to real time). After initial operations of front-end processing of the three signals, described below with reference to
Since the fundamental signals are sampled discretely, as opposed to continuously, the Nyquist sampling criteria applies. As is known to a skilled artisan, the Nyquist frequency is half the sampling frequency of any discrete signal processing system, and signal aliasing will occur at frequencies higher than the Nyquist frequency. A final sampling rate for each of the three fundamental signals (pT, pM, and DC) as described herein is two times (2×) the heart rate, which means that theoretically information content can be captures up to a value of β=1.0. However, because the information content carried by each of the three fundamental signals is inherently equivalent to sampling at only one times (1×) the heart rate, the effective Nyquist frequency (above which aliasing occurs) is β=0.5, and a respiration rate greater than half the heart rate cannot be measured. For spontaneous breathing in human subjects, the heart rate is typically four times (4×) to five times (5×) the respiration rate, and being limited to detecting respiration rates less than half the heart rate is not a significant limitation in practice.
With reference now to
In
At 205 a low-pass filter eliminates much of the high-frequency signal due to the cardiac pulses and passes primarily the low-frequency signal caused by the respiration of the subject.
At 207, the signal received from 205 is passed through a first high-pass filter. In an embodiment, the first high-pass filter may have an exponential-type averaging function to provide a smoothing of the input data. Such high-pass filter types are known in the art (e.g., such as a DC blocker). This embodiment may also use the high-pass filter with a p-value of 0.00.
With regard to p-values, for a given digital signal X[n], where n is the sample number, a high-pass filtered value, D[n], (commonly referred to as a DC blocker), used in various embodiments described herein, can be categorized by a p-value as given by the mathematical equation:
D[n]=X[n]−X[n−1]+p*D[n−1]
where the parameter p satisfies the condition: 0≤p<1.
A corresponding Z-domain transfer function H(Z) is then given by:
H(Z)=D(Z)/X(Z)=[1−(Z−1)]/[1−p*(Z−1)]
which has a Zero located at Z=1 (DC), and a pole located at Z=p.
Additional determinations for p-values are described in more detail, below. If digital-filtering techniques are employed, the skilled artisan will recognize that various types of techniques may be used to smooth the data for each of the filtering steps described herein. For example, higher-degree polynomial fits, z-transfer functions, pulse transfer functions, moving average functions, and so on are known in the art.
At 209, the output of the first high-pass filter is averaged over one pulse at a time. In this embodiment, the pulse is shifted by one-half pulse at a time over a sampling pulse-time frequency of two heart rate (HR) pulses. The pulse shifting technique is described in more detail with reference to
At 211, the resulting signal output from 209 is passed through a second high-pass filter having, for example, a p-value of 0.50. A front-end processed signal of DC0 is output at 213 from the DC digital-signal filtering method 200. In various embodiments, all but the p=0 value used in the DC signal can be determined empirically to increase or maximize SNR across the subject population. The p=0 value for the DC, when combined with averaging over one pulse, has a very special property that it produces a transfer function which depends only on β, and not on actual frequency.
The two high-pass filtering steps help reduce or eliminate frequencies due to Mayer waves, discussed above with reference to
In
With concurrent reference to
At 237, the signal passes through a first high-pass filter. In an embodiment, the first high-pass filter has a p-value of 0.95. In one embodiment, all of the preceding operations are carried out at 1×HR. At operation 239, the signal is up-sampled. Up-sampling reduces artifacts (beat effects due to phase sensitivity) that would otherwise occur in the waveform as β approaches 0.5. An increased sampling frequency therefore captures proper phase information and therefore reduces or eliminates possible issues due to phase. In a specific exemplary embodiment, the signal is up-sampled to about double the frequency at 2×HR. The up-sampled frequency is then sent through a second high-pass filter at 241. In an embodiment, the second high-pass filter has a p-value of 0.50.
Front-end processed signals of pT0 and pM0 are output at 243, 263, respectively, from the digital-signal filtering methods 230, 250.
In addition to the three front-end processed signals of DC0, pT0, and pM0, a fourth fundamental input used in later processing, discussed below with reference to
After this point, all further signal processing is performed in pulse time and not in real time. By performing all additional calculations in pulse time, fewer numbers of bandpass filters can be used since a total calculation range is determined quickly by relying on pulse-time calculations. The signal bandwidth of interest is then determined automatically by using the pulse-time calculations.
The skilled artisan will have recognized the use of the two high-pass filters in each of
Referring now to
With continued reference to
At 305, 307, an interpolation of positive and negative zero-crossings is determined and a median value of the breath rate is calculated for each of the positive and the negative edges. Calculation of the median value can be considered an application of the median filter.
In an embodiment, the median period of the breath rate, based on the positive zero-crossings and determined from considering three breaths up to nine breaths, is given by equation (2):
<bT>P=bT Median[3,9](sec) (2)
The median period of the breath rate, based on the negative zero-crossings, is given by equation (3):
<bT>P=bT Median[3,9](sec) (3)
In this embodiment, a three-breath minimum is used to eliminate outliers in a subject's breathing pattern and, consequently, increase accuracy of the determined breath rate by reducing noise caused by breath-to-breath variations in the subject's breathing. The nine-breath maximum was determined experimentally as providing a consistent median value of breath rate that is consistent with a subject's actual breathing rate in most subjects. Additional experimental measurements have determined that some subjects have extremely consistent pulse rates—pulse rates have been observed within a root-mean-square (RMS) variation as small as three milliseconds and as large as 60 msec. However, the inventive subject matter described herein has been established based on being applicable to the entire population.
Equations (2) and (3) typically produce slightly different results since both the phase is slightly different and the duty cycle is changing.
The average breaths per minute, BPMP, based on the breath time between the positive zero-crossings, is given by equation (4):
<BPM>P=60/<bT>P (4)
In a similar fashion, average breaths per minute, BPMN, based on the breath time between the positive zero-crossings, is given by equation (5):
<BPM>N=60/<bT>N (5)
The average number of breaths per minute, BPM, is then determined as an arithmetic average of the positive average breaths per minute, BPMP and BPMN, according to equation (6):
<BPM>=½[<BPM>P+<BPM>N] (6)
By determining the approximate breath rate by processing and calculating zero-crossings as shown above, an initial time to first display the breath rate of the subject, in accordance with this embodiment, occurs after only four positive edges and four negative edges. The time to display the breath rate with most subjects then is approximately 15 seconds. Further, the computational requirements are very limited. For example, a processor with a limited computational speed can readily perform the calculations shown above to determine and display an initial approximation of breath rate of a subject. However, the approximation of breath rate is still accurate with little variation from much more involved methodologies, for example, as described with reference to the adaptive-filtering techniques, below.
As discussed above with regard to
N
1A
=N
1/2
Similarly, the second half of the first pulse 401 has a total number of samples, N1B, of the total number of samples, N1, for the entire first pulse, is ½ of the total number of samples and is calculated as:
N
1B
=N
1/2
Similar calculations are made for the second pulse 403, with each summation being shifted one-half pulse at a time. A skilled artisan will immediately recognize that other portions of the pulses can be determined and calculated that are not ½ pulse portions being unpatentable over, rather, some other fractional amount or amounts.
From this information, a running DC average, determined as a continuous function <F>, for each time, t, in a period, T, over a predefined number of pulses is then calculated as:
For the discrete values sampled, an average DC signal for each of the Y pulse incremental ranges, 405, 407, 409, shown in
Thus, <DC>0 is calculated as the average of the half-pulse summations divided by the number of samples over the entire first pulse, <DC>1 is calculated as the average of the half-pulse summations over the second half of the first pulse and the first half of the second pulse, divided by the number of samples over that pulse range, and <DC>2 is calculated as the average of the half-pulse summations by the number of samples over the entire second pulse.
Consequently, at twice the heart rate, the average signal of DC0 411 for the PPG can be determined. When combined with the high-pass filtering shown and described above with reference to
With reference now to
The graph 530 of
A skilled artisan will recognize that a smaller number or a larger number of bandpass filter may be employed for finding the central frequency of a signal. A smaller number of bandpass filters increases computational speeds with some sacrifice in accuracy. A smaller number of bandpass filters will also have an effect on
Referring again to
A skilled artisan will recognize that a cosine-squared windowing function is employed by equation (7) to reduce or eliminate any or most spectral leakage.
In this embodiment, each bandpass filter is a Type 2 (odd/anti-symmetric) linear phase filter. As noted above, each bandpass filter has 29 taps at twice the heart rate—extending over 14.5 pulses and having the same phase delay at all frequencies. Since the bandpass filters, by virtue of being linear phase filters, or approximately a linear-phase filter, each have the same phase delay, outputs from each of the bandpass filters can be added directly. Being able to add outputs directly can save considerable computational time as will be discussed in more detail below.
Referring now to the method 600 of FIG. B, for each of the three fundamental signals, at 601, the averaged RMS amplitude, Ai, output from each filter, BPi, is calculated. The summation for each calculated value of Ai is then normalized to 1.0 for further processing at 603. At 605, each of the Ai values is equalized according to the three fundamental signal types. A quadratic interpolation is then performed to calculate βMAX for each of three fundamental signal types. Consequently, the method 600 determines at what value of β is the peak response present for each of the three fundamental signal types. The determined and calculated value for βMAX for each of three fundamental signal types is then combined into a merged spectrum for further processing.
The method 630 of
From the methods 600, 630 of
As an example of applying the method, there is an equalized spectrum for each of the three fundamental signals (pT, pM, and DC), and also a merged spectrum, each of which is comprised of the 13 band-pass filters. For each of those four spectra, a βMAX value is calculated every half pulse as follows:
For verification of the inventive subject matter, a determination was made whether the calculated values of β, both from the fixed-filtering algorithm of
A time-dependent spectrum graph 730 of
The skilled artisan will detect some deviation from the regression lines 801, 803, 805, 807 at approximately 0.05 to 0.10 on the ordinate axis. These deviations are caused by Mayer waves, discussed above with reference to
The skilled artisan will also note the “t-shaped” spread in the spectra of a β value of approximately 0.5. The spread is due to an aliasing effect as described herein. However, as also described herein with regard to most human subjects, a typical respiration rate is much less than one-half the heart pulse rate. Therefore, the aliasing effect seldom, if ever, has an impact in calculating a value of β for a given subject.
The inputs 1001 include the normalized Merged spectral amplitudes or “M” values (M0.00 to M0.06) of the outputs of the 13 bandpass filters from the merged spectrum as shown and described with reference to
The inputs 1001 are input to the second-order surface response function 1003. In an embodiment, 45 terms (based on the 18 input-factors as noted immediately above) are used to calculate an output of the second-order surface response function 1003, an output of which is the transfer function estimate of β, βXF, 1005. The determined transfer function estimate βXF indicates the center value of the signal, β, for choosing the adaptive filer.
Referring again to the second-order surface response function 1003, the skilled artisan will recognize that, based on the 18 input values, 190 factors can be calculated. For example, considering only a two-factor input, i1 and i2, the surface response function would include a first-order function, i1+i2. The second order response function would include i1+i2, i1×i2, i12, and i22. As such, a response surface methodology (RSM), in general, considers relationships between a number of input variables and one or more resulting response variables. The RSM can be used in a design-of-experiments to estimate an optimal response function. The skilled artisan will further recognize that a larger or smaller number of factors may be employed depending upon a desired accuracy of β. Waveform examples with dynamic β estimates are described and shown with reference to
In
βEST=½[βXF+βWF]
In a specific exemplary embodiment, once the new estimate based on the waveforms, βWF, is then fed back at 1013 (e.g., about 15 pulses after the first estimate of βXF), the “loop” runs continuously in time. At 1015, a signal fusion occurs where the predicted respiration rate, pRR, is determined as a median value of the respiration rates as determined for each of the three signals DC, pT, and pM, as described and shown with reference to
In an embodiment, when an estimate of β has been determined in accordance with various aspects of the inventive subject matter described herein, the bandpass filter closest to the estimate, along with two-nearest neighbors (that is, a total of three bandpass filters), are utilized in processing the waveform for a given signal type. The zero-crossings (considering both positive-edge zero-crossing and negative-edge zero-crossings) may then be used to determine the respiration rates of a subject. Along with consideration of the three fundamental signals, the actual zero-crossings can provide yet a further estimate of the actual value of β. The combined-β estimate (transfer function plus feedback) performs better than either value used alone. As described with reference to
Clinical trials have indicated that accurate values of β, and consequently respiration rate, can be determined quickly and accurately. For example, using the fixed (non-adaptive) filter algorithm described and shown with reference to
With reference now to
The computer system 1200 includes a processor 1201 (e.g., a hardware-based microprocessor or embedded hardware-based processor, a hardware-based central processing unit (CPU), a hardware-based graphics processing unit (GPU), or various combinations thereof), a main memory 1203 and a static memory 1205, which communicate with each other via a bus 1207. The computer system 1200 may further include a video display unit 1209 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 1200 also includes an alphanumeric input device 1211 (e.g., a keyboard), a user interface (UI) navigation device 1213 (e.g., a mouse), a disk drive unit 1215, a signal generation device 1217 (e.g., a speaker), and a network interface device 1219.
The disk drive unit 1215 includes a non-transitory machine-readable medium 1221 on which is stored one or more sets of instructions and data structures (e.g., software 1223) embodying or used by any one or more of the methodologies or functions described herein. The software 1223 may also reside, completely or at least partially, within the main memory 1203 or within the processor 1201 during execution thereof by the computer system 1200; the main memory 1203 and the processor 1201 also constituting machine-readable media.
While the non-transitory machine-readable medium 1221 is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more instructions. The term “non-transitory machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention, or that is capable of storing, encoding, or carrying data structures used by or associated with such instructions. The term “non-transitory machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including by way of exemplary semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices); magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.
The software 1223 may further be transmitted or received over a communications network 1225 using a transmission medium via the network interface device 1219 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, mobile telephone networks, Plain Old Telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.
Included in the disclosed subject matter provided herein are various system and method diagrams describing various embodiments of the particulate matter sensor calibration system. Therefore, the description above includes illustrative examples, devices, systems, and methods that embody the disclosed subject matter. In the description, for purposes of explanation, numerous specific details were set forth in order to provide an understanding of various embodiments of the inventive subject matter. It will be evident, however, to those of ordinary skill in the art that various embodiments of the inventive subject matter may be practiced without these specific details. Further, well-known structures, materials, and techniques have not been shown in detail, so as not to obscure the various illustrated embodiments. For example, the skilled artisan will recognize that each of the filtering algorithms described herein can be implemented in hardware, software, firmware, or various combinations thereof. Also, the various filters can be analog filters in addition to digital filters, or a combination of the two.
In accordance with the present disclosure, components, process steps, and/or data structures may be implemented using various types of operating systems, programming languages, computing platforms, computer programs, and/or general-purpose machines. In addition, those of ordinary skill in the art will recognize that devices of a less general purpose or nature, such as hardwired devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), or the like, may also be used without departing from the scope of the concepts disclosed herein. For example, the skilled artisan will recognize that one or more of the filter described herein can be implemented in an FPGA device. As also described herein, various embodiments may be tangibly embodied as a set of computer instructions stored on a computer readable medium, such as a memory device.
As used herein, the term “or” may be construed in an inclusive or exclusive sense. Additionally, although various exemplary embodiments discussed herein focus on particular ways to determine an estimate of β, other embodiments will be understood by a person of ordinary skill in the art upon reading and understanding the disclosure provided. Further, upon reading and understanding the disclosure provided herein, the person of ordinary skill in the art will readily understand that various combinations of the techniques and examples provided herein may all be applied in various combinations.
Although various embodiments are discussed separately, these separate embodiments are not intended to be considered as independent techniques or designs. As indicated above, each of the various portions may be inter-related and each may be used separately or in combination with other particulate matter sensor calibration system embodiments discussed herein.
Consequently, many modifications and variations can be made, as will be apparent to the person of ordinary skill in the art upon reading and understanding the disclosure provided herein. Functionally equivalent methods and devices within the scope of the disclosure, in addition to those enumerated herein, will be apparent to the skilled artisan from the foregoing descriptions. Portions and features of some embodiments may be included in, or substituted for, those of others. Such modifications and variations are intended to fall within a scope of the appended claims. Therefore, the present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.
The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. The abstract is submitted with the understanding that it will not be used to interpret or limit the claims. In addition, in the foregoing Detailed Description, it may be seen that various features may be grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as limiting the claims. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.
